Data Engineering Lead

Roles and responsibilities :

• Design end-to-end data solutions, including data lakes, lakehouses, real-time streaming pipelines, ETL / ELT workflows, and data integration architectures.
• Ensure modularity, scalability, and interoperability for enterprise-wide data ecosystems across hybrid and multi-cloud environments.
• Build and optimize DataOps frameworks to improve pipeline automation, data quality, and observability.
• Collaborate with engineering teams to reduce data latency, optimize compute / storage costs, and ensure high availability and reliability.
• Implement data governance frameworks, including data lineage, cataloging, access controls, and quality metrics.
• Ensure compliance with GDPR, CCPA, ISO 27001, and other relevant standards for privacy, security, and ethical data use.
• Partner with Sales, Business Development, and Delivery teams to craft data engineering solutions aligned with client needs.
• Support RFP responses, pricing models, and deal structuring with robust cost modeling and architecture justification.
• Lead client workshops, PoCs, and solution demonstrations to showcase TP.Ai Data Services capabilities.
• Work with Finance and Product teams to develop pricing strategies based on cost-to-serve, consumption, and value-driven models for data platforms and managed services.
• Benchmark against industry pricing trends to ensure competitiveness.
• Stay at the forefront of emerging data technologies (orchestration frameworks like Airflow, Dagster, Prefect, data mesh architectures, AI-ready data pipelines).
• Provide internal training and external advisory on data engineering best practices, modernization strategies, and ROI-driven data transformation.
• Engage with AI / ML, Security, Operations, and Trust & Safety teams to ensure integrated, compliant, and future-proof data environments.

Preferred Qualifications :